Classification of α wave motor imagery based on SVM and PCA - Cai Jing; Liu Guangda; Wang Yaoyao; Gong Xiaoyu (Dianzi Jishu Yingyong, 2022)
A feature screening method based on alpha wave and principal component analysis was proposed to solve the problem that the weakly correlated feature quantity would affect the classification accuracy in EEG motor imagery classification. Based on brain computer interface system, the EEG signals corresponding to left and right motor imagination tasks were generated by auditory stimulation and processed by wavelet packet decomposition, and then the α band signals of the EEG were reconstructed, so as to extract the α waveforms and extract the statistical features. Combined with PCA technology and SVM method, the weak correlation features are eliminated and classified. According to the selected data, the accuracy of the results is higher, and the accuracy of signal classification is improved from 90.1% to 94.0%.